Integrating computing in the statistics and data science curriculum: Creative structures, novel skills and habits, and ways to teach computational thinking

TL;DR

This paper emphasizes the importance of integrating computing into statistics and data science education through creative curriculum structures, novel skills, and teaching methods for computational thinking.

Contribution

It presents new approaches and strategies for embedding computational skills and thinking into the statistics and data science curriculum.

Findings

Creative curriculum structures for computing integration

Novel data science skills and habits

Proposed methods for teaching computational thinking

Abstract

Nolan and Temple Lang (2010) argued for the fundamental role of computing in the statistics curriculum. In the intervening decade the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the *Journal of Statistics and Data Science Education* we have assembled a collection of papers that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these papers provide useful guidance for the community to move these efforts forward.